Update README.md

README.md

@@ -21,4 +21,9 @@ Here are some perplexity measurements:
 | [QAT Q4_0 (google)](https://huggingface.co/google/gemma-3-1b-it-qat-q4_0-gguf/blob/main/gemma-3-1b-it-q4_0.gguf) | 1 GB | 28.0400 +/- 0.26669 |
 | [BF16 (upscaled to f32 for faster inference)](https://huggingface.co/google/gemma-3-1b-it) | 2 GB | 29.1129 +/- 0.28170 |
 
-Note that this model ends up smaller than the Q4_0 from Bartowski. This is because llama.cpp sets some tensors to Q4_1 when quantizing models to Q4_0 with imatrix, but this is a static quant.
+Note that this model ends up smaller than the Q4_0 from Bartowski. This is because llama.cpp sets some tensors to Q4_1 when quantizing models to Q4_0 with imatrix, but this is a static quant.
+
+I also fixed the control token metadata, which was slightly degrading the performance of the model in instruct mode. Shoutout to ngxson for [finding the issue](https://huggingface.co/google/gemma-3-12b-it-qat-q4_0-gguf/discussions/3#67f6a2e0207b4bceea793151),
+tdh111 for [making me aware of the issue](https://huggingface.co/stduhpf/google-gemma-3-27b-it-qat-q4_0-gguf-small/discussions/3#67f74fdf8411d4d6a82049db), 
+and u/dampflokfreund on reddit ([Dampfinchen](https://huggingface.co/Dampfinchen) on Huggingface) for [sharing the steps to fix it](https://www.reddit.com/r/LocalLLaMA/comments/1jvi860/comment/mmcuvw2).
+That model still struggles at long context with these fixes (just like the original qat model).